The performance of a vehicle and its systems is often strongly dependent on the current driving context. This is also true for certain vehicle control systems such as those for the engine and chassis that perform differently under varying driving conditions. Driver-vehicle interaction systems, often referred to as intelligent driver support systems, are also affected by current driving conditions and are most effective when certain key conditions are assessed and considered in their operation. Thus, if certain driving patterns associated with a particular driving situation could be detected, parameters of these dependent systems could be optimized, preferably on a real time basis. These possibilities have attracted substantial interest that has been fuelled by the rapid development of sensors capable of measuring relevant vehicle performance characteristics, as well as key driver behaviors or activities.
There are many different types of driving patterns that can be relevant to particular vehicle system performance. In this regard, a general distinction is made herein between large time-scale and small time-scale driving patterns. The latter category includes specific events or maneuvers as overtaking or passing, turning, and changing lanes. Pioneering work in this area focused on recognizing and predicting driving practices or maneuvers. By contrast, large time-driving patterns refer to more general driver characteristics and driving conditions. Examples of such driver characteristics include those related to the driver's mental state such as being drowsy, distracted, impaired because of health reasons or being under the influence of chemical substances, or attentive and focused on the driving task. Examples of relevant driving conditions or environments are city driving, highway driving and suburban driving.
Existing approaches to the detection of large-patterns associated with the driving road type/environment are problematic. One way to obtain knowledge of such large-driving contexts is by means of a global positioning system (“GPS”) coupled with a map database where the geographical positions are tagged according to the desired scheme. While this approach has certain advantages, in particular its possibilities for predictive capacities, a major drawback of the scheme is the costly and high labor need for manual tagging and maintenance of the database at varying levels of detail. Moreover, since the knowledge is not based on real-time empirical data, accuracy can be limited for such reasons as variations in traffic density.
An alternative approach is to infer the current driving context directly from patterns of data obtained from vehicle sensors. Exemplary sensors are those for speed, gear-shift position, turn indicator activity, steering wheel angle, and braking activity. For example, it may be expected that driving in a city is characterized by low travel speeds having high variability and frequent brake use. Perhaps the simplest such approach is to construct a set of rules, for example “if vehicle speed is greater than 90 kilometers per hour, the current driving context is ‘highway’”. A basic limitation of this approach, however, is the difficulty in formulating, ad-hoc, such exact definitions of the target categories. An approach to this problem has been to use fuzzy logic in which the target categories are treated as fuzzy sets where membership is a matter of degree, rather than exact definitions.
In view of the deficiencies associated with these known approaches for assessing driving conditions, there is still a need for more useful systems and methods for performing real-time recognition of large scale driving patterns. More particularly, the present invention(s) looks to statistical pattern recognition frameworks that utilize models that learn the desired classification scheme from empirical data and recognize predefined categories of large time-scale driving patterns.